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Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.

Objectives: This study aims to comprehensively explore trends, sentiments, and visualization of mental stress expressions in online communities, focusing on discussions within subreddits on the social media platform Reddit. Methods/Analysis: Advanced text analysis and statistical techniques are employed to achieve the study’s objectives. The research utilizes natural language processing (NLP) methods, sentiment analysis, and topic modeling to unravel the intricate layers of mental stress expressions found in posts across diverse subreddits. Additionally, engagement metrics, such as Redditors’ scores and the number of comments, are analyzed to discern distinctive information and patterns of interest. Findings: The research sheds light on prevalent trends, sentiments, and themes related to mental stress in online conversations before and after January 2020. The findings provide valuable insights into patterns of exciting topics, shared experiences of stress, coping mechanisms, and the significant role of virtual communities in offering support and understanding. Novelty/Improvement: The novelty lies in applying advanced text analysis techniques, including sentiment analysis with the majority voting method combining different machine learning techniques and topic modeling with semantic networks, to gain a deeper understanding of the dynamics of mental stress expressions in online communities. The research explores current patterns and distinguishes itself by examining temporal variations in stress-related posts and their correlation with engagement metrics, offering an innovative perspective on mental health discussions in the digital age. Doi: 10.28991/HEF-2024-05-02-01 Full Text: PDF

A β-Li3PS4 solid electrolyte was prepared via liquid-phase synthesis using ethyl isobutyrate as a synthetic medium. The precursor and solid electrolyte structures were characterized using thermogravimetry–differential thermal analysis and X-ray diffraction techniques. The dielectric relaxation analysis showed two relaxation regions, which revealed a bulk and grain boundary ionic migration process. At a temperature lower than 90°C, the frequency dependence of the dielectric constant of the prepared sample was different from that of glassy Li3PS4, indicating that the motion of the PS4 unit enhances the ionic conductivity of the Li3PS4 solid electrolyte. The ionic conductivities of the cold-pressed and warm-pressed pellets at 25°C were 6.8 × 10−5 Scm−1 and 3.6 × 10−4 Scm−1, respectively.

Ruptured aneurysmal subarachnoid hemorrhage associated with multiple basilar trunk aneurysms represents a rare clinical condition. Endovascular intervention stands as the preferred therapeutic approach. We present the case of a 35-year-old patient with subarachnoid hemorrhage and three consecutive basilar trunk aneurysms. Utilizing a flow-diverter stent, we achieved simultaneous occlusion of all 3 aneurysms, performed 2 hours post dual antiplatelet therapy (comprising salicylic acid 300 mg and ticagrelor 180 mg). Sustained resistance to clopidogrel necessitated the subsequent 3 months, followed by single antiplatelet therapy. At the 1-month follow-up, the patient demonstrated a favorable clinical course, devoid of cerebral infarction, and evidenced unobstructed stent patency upon brain magnetic resonance imaging.

Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.

African swine fever virus (ASFV) genotype II is endemic to Vietnam. We detected recombinant ASFV genotypes I and II (rASFV I/II) strains in domestic pigs from 6 northern provinces in Vietnam. The introduction of rASFV I/II strains could complicate ongoing ASFV control measures in the region.

Objectives: This study explores different machine learning models (KNN: k-nearest neighbor, MLP: Multilayer Perceptron, SVM: Support Vector Machine) to identify the optimal model for accurate and rapid mental health detection among the recovered COVID-19 patients. Other techniques are also investigated, such as feature selection (Recursive Feature Elimination (RFE) and Extra Trees (ET) methods) and hyper-parameter tuning, to achieve a system that could effectively and quickly indicate mental health. Method/Analysis: To achieve the objectives, the study employs a dataset collected from recovered COVID-19 patients, encompassing information related to depression, anxiety, and stress. Machine learning models are utilized in the analysis. Additionally, feature selection methods and hyper-parameter tuning techniques are explored to enhance the model’s predictive capabilities. The performance of each model is assessed based on accuracy metrics. Findings: The experimental results show that SVM is the most suitable model for accurately predicting an individual’s mental health among recovered COVID-19 patients (accuracy ≥ 0.984). Furthermore, the ET method is more effective than the RFE method for feature selection in the anxiety and stress datasets. Novelty/Improvement:The study lies in the understanding of predictive modeling for mental health and provides insights into the choice of models and techniques for accurate and early detection. Doi: 10.28991/HEF-2024-05-01-01 Full Text: PDF

The steel pony-truss bridge is a structure commonly used in road and rail traffic. It has a simple structure, a large bearing capacity, and high economic efficiency. The safety of steel pony-truss bridges is highly dependent on the stability conditions of the top chord. This paper presents the reliability assessment of the compressive top chord of steel pony truss bridges according to stability conditions. For this purpose, a deterministic model was built from the stability conditions of the compressive top chord; then, a stochastic model was established based on the deterministic model, in which the structural geometries and material properties are random parameters. Finally, the reliability of the structure is evaluated using Hypercube Latin sampling and Monte-Carlo simulation.

Anh Tuan Tran,1– 3,* Dinh Minh Nguyen,4,* Quang Hien Tran,5,6 Quang Hung Nguyen,7 Thu Ha Nguyen Thi,1 Do Quyen Tran Thi,1 Hong Nhung Luu,1 Yen Mai Do Thi1 1Radiology Center, Bach Mai Hospital, Hanoi, Vietnam; 2Radiology Department, University of Medicine and Pharmacy, Hanoi National University, Hanoi, Vietnam; 3Stroke and Brain Vascular Disease Department, University of Medicine and Pharmacy, Hanoi National University, Hanoi, Vietnam; 4Radiology Department, Viet Duc Hospital, Hanoi, Vietnam; 5An Giang Department of Health, An Giang, Vietnam; 6Obstetrics and Gynecology, an Giang Women’s and Children’s Hospital, An Giang, Vietnam; 7Oncology and Nuclear Medicine Center, Bach Mai Hospital, Hanoi, Vietnam*These authors contributed equally to this workCorrespondence: Quang Hien Tran, An Giang Department of Health, Obstetrics and Gynecology, an Giang Women’s and Children’s hospital, 15 Le Trieu Kiet Street, Long Xuyen City, An Giang, Vietnam, Tel +84913104293, Email tranquanghienag@yahoo.comObjective: To evaluate the effectiveness of ultrasound-guided needle aspiration in treating lactating breast abscesses.Methods: This study was conducted in Bach Mai Hospital, from 6/2020 to 7/2021. Lactating patients with breast abscesses underwent ultrasound-guided aspiration followed by antibiotics therapy.Results: There were 59 lactating patients with 82 breast abscesses. Most of the abscesses had heterogeneous echogenicity, no capsule, and a size smaller than 5cm. Bacterial culture results showed that 85.4% of cases were Methicillin-resistant Staphylococcus aureus. The number of aspirations was from 1 to 5. The cure rate was 91.5%, and 5.3% of these cases had a complication associated with galactocele after treatment.Conclusion: Ultrasound-guided needle aspiration is a minimally invasive treatment option for lactating breast abscesses with a high complete cure rate and good cosmetic results.Keywords: breast abscess, ultrasound-guided needle aspiration

The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.

This study addresses the compelling need for emerging-market business organizations to excel in turbulent environments. Drawing upon a dynamic capability framework, we delve into organizational mindfulness, resilience, and performance dynamics within emerging-market contexts. Through a moderated mediation model, we investigate the impact of organizational mindfulness on organizational performance, which is mediated by organizational resilience. Additionally, we explore the moderating role of digitalized management accounting systems (MASs) use in enhancing the relationship between organizational mindfulness and organizational resilience. Utilizing partial least squares structural equation modeling (PLS-SEM), we analyze data from a two-phase survey involving 441 managers in Vietnamese organizations. Results confirm organizational resilience as a vital mediator between organizational mindfulness and organizational performance, and digitalized MAS use positively moderates the effect of organizational mindfulness on organizational resilience. This research adds to the literature on the interface between mindfulness and resilience and informs practical strategies for navigating uncertainty. By studying the interplay among mindfulness, resilience, and digitalization, we provide guidance for emerging-market organizations aiming to respond to environmental turbulence successfully.

In the large-scale deployment of federated learning (FL) systems, the heterogeneity of clients, such as mobile phones and Internet of Things (IoT) devices with different configurations, constitutes a significant problem regarding fairness, training performance, and accuracy. Such system heterogeneity leads to an inevitable trade-off between model complexity and data accessibility as a bottleneck. To avoid this situation and to achieve resource-adaptive FL, we introduce CrossHeteroFL to deal with heterogeneous clients equipped with different computational and communication capabilities. Our solution enables the training of heterogeneous local models with additional computational complexity and still generates a single global inference model. We demonstrate several CrossHeteroFL training scenarios and conduct extensive empirical evaluation, covering four levels of the computational complexity of three-model architectures on two datasets. The proposed mechanism provides the system with non-elementary access to a scattered fit among clients. However, the proposed method generalizes soft handover-based solutions by adjusting the model width according to clients’ capabilities and a tiered balance of data-source overviews to assess clients’ interests accurately. The evaluation results indicate our method solves the challenges in previous studies and produces greater top-1 accuracy and consistent performance under heterogeneous client conditions.

Medical image segmentation is crucial for deep learning (DL) applications in clinical settings. Ensuring accurate segmentation is challenging due to diverse image sources and significant data sharing and privacy concerns in centralized learning setups. To address these challenges, we introduce a novel federated learning (FL) framework tailored for breast cancer. First, we use random regions of interest (ROIs) and bilinear interpolation to determine pixel color intensity based on neighboring pixels, addressing data inconsistencies from heterogeneous distribution parameters and increasing dataset size. We then employ the UNet model with a deep convolutional backbone (Visual Geometry Group [VGG]) to train the augmented data, enhancing recognition during training and testing. Second, we apply the Gaussian Mixture Model (GMM) to improve segmentation quality. This approach effectively manages distinct data distributions across hospitals and highlights images with a higher likelihood of tumor presence. Compared to other segmentation algorithms, GMM enhances the salience of valuable images, improving tumor detection. Finally, extensive experiments in two scenarios, federated averaging (FedAvg) and federated batch normalization (FedBN), demonstrate that our method outperforms several state-of-the-art segmentation methods on five public breast cancer datasets. These findings validate the effectiveness of our proposed framework, promising significant benefits for the community and society.

Background: Although Blockchain Technology (BT) is one of the innovations that has considerable potential to improve business processes and enable new services for potential users, its implementation in supply chain management (SCM) of the automotive industry is only at its beginnings. From the growing number of publications focused on this issue, it is evident that the application of BT would significantly contribute to the development of the automotive industry and improve the supply chain of automotive components. Purpose: In this regard, the paper aims to analyze the challenges to the implementation of BT in SCM in the automotive industry sector through a systematic review of the literature and precise definition of the advantages and limitations that appear in supply chains after the application of BT. Study design/methodology/approach: The research is based on the application of systematic literature review methods. The paper presents the results and conclusions of 21 studies based on the search criteria outlined by the Web of Science, Scopus, and SpringerLink index databases. Findings/conclusions: The results suggest that insufficiently developed technology, lack of clear guidelines for implementation, incomplete standardization, legislative ambiguity, conflicts and insufficient cooperation between chain members appear as the biggest challenges for BT implementation. On the other hand, BT has great potential in reducing costs, providing higher quality products and services, and improving chain visibility in the automotive industry. Limitations/future research: The analysis of the papers in the above mentioned databases exclusively in English and the absence of empirical research stand out as the most prominent shortcomings. However, the obtained results of this study represent a quality basis for future research, which, judging by the popularity of the issue, will increase in frequency.

ABSTRACTThis paper presents an energy-saving and lifetime-maximizing solution for the LoRa wireless mesh network (WMN). Energy dissipation is a crucial factor affecting the usability of the LoRa WMN. In the worst cases, in systems without electric mains, the life of a sensor node battery may last for only a few hours. Two proposed solutions are characterized as energy-saving due to the use of deep sleep in the ESP8266-12F microcontroller. This allows the optimization of duty cycling, which refers to the ratio between active and inactive periods of sensor nodes power-gating the node, i.e. turning off all circuitries. This solution benefits applications using active power-hungry sensors sampled many times daily. Notably, reducing power consumption during idle time increases the optimal battery life by up to hundreds of times. As a result, the automatic uptime of a LoRa WMN can increase from days to months or even years, depending on usage. Therefore, energy-saving must be optimized if the node is to be installed in locations without a grid or renewable energy source. Experimental results show that the proposed energy-saving solution is more effective than those introduced by previous studies.

Abstract Background The demand for 68Ga-labeled radiotracers has significantly increased in the past decade, driven by the development of diversified imaging tracers, such as FAPI derivatives, PSMA-11, DOTA-TOC, and DOTA-TATE. These tracers have exhibited promising results in theranostic applications, fueling interest in exploring them for clinical use. Among these probes, 68Ga-labeled FAPI-46 and DOTA-TOC have emerged as key players due to their ability to diagnose a broad spectrum of cancers ([68Ga]Ga-FAPI-46) in late-phase studies, whereas [68Ga]Ga-DOTA-TOC is clinically approved for neuroendocrine tumors. To facilitate their production, we leveraged a microfluidic cassette-based iMiDEV radiosynthesizer, enabling the synthesis of [68Ga]Ga-FAPI-46 and [68Ga]Ga-DOTA-TOC based on a dose-on-demand (DOD) approach. Results Different mixing techniques were explored to influence radiochemical yield. We achieved decay-corrected yield of 44 ± 5% for [68Ga]Ga-FAPI-46 and 46 ± 7% for [68Ga]Ga-DOTA-TOC in approximately 30 min. The radiochemical purities (HPLC) of [68Ga]Ga-FAPI-46 and [68Ga]Ga-DOTA-TOC were 98.2 ± 0.2% and 98.4 ± 0.9%, respectively. All the quality control results complied with European Pharmacopoeia quality standards. We optimized various parameters, including 68Ga trapping and elution, cassette batches, passive mixing in the reactor, and solid-phase extraction (SPE) purification and formulation. The developed synthesis method reduced the amount of precursor and other chemicals required for synthesis compared to conventional radiosynthesizers. Conclusions The microfluidic-based approach enabled the implementation of radiosynthesis of [68Ga]Ga-FAPI-46 and [68Ga]Ga-DOTA-TOC on the iMiDEV™ microfluidic module, paving the way for their use in preclinical and clinical applications. The microfluidic synthesis approach utilized 2–3 times less precursor than cassette-based conventional synthesis. The synthesis method was also successfully validated in a similar microfluidic iMiDEV module at a different research center for the synthesis of [68Ga]Ga-FAPI-46 with limited runs. Our study demonstrated the potential of microfluidic methods for efficient and reliable radiometal-based radiopharmaceutical synthesis, contributing valuable insights for future advancements in this field and paving the way for routine clinical applications in the near future.

Dong Nai River provides essential water resources for millions of people across 11 provinces and cities in Vietnam. However, the different pollution sources such as household, farming, and industrial operations have caused the river water quality to deteriorate over the past decade. To gain a comprehensive understanding of the river's surface water quality, this study employed the water quality index (WQI) across 12 different sampling sites. In total,144 water samples with 11 parameters were analyzed in accordance with the Vietnamese standard 08:2015/MONRE. Results revealed a range of surface water quality, from poor to good according to the VN-WQI (Vietnamese standard), and a medium even bad level in some months according to the NS-WQI (American standard). The study also identified temperature, coliform, and dissolved oxygen (DO) as strong contributors to WQI values (VN_WQI standard). Principal component analysis/factor analysis was used to determine pollution sources, with the results highlighting agricultural and domestic activities as the main contributors to river pollution. In conclusion, this study underscores the importance of effective planning and management of infrastructure zoning and local activities to improve the river's surface water quality and surrounding areas, as well as safeguard the well-being of the millions who depend on it. HIGHLIGHTS Surface water quality of the Dong Nai River varies from poor to good according to the VN-WQI and medium to bad according to the NS-WQI.; Agricultural and domestic activities are the main contributors to river pollution according to the study's results.; The study's findings could assist researchers and policymakers in taking comprehensive actions for sustainable water management.;

In this study, we present a novel approach to injection molding, focusing on the strength of weld lines in polyamide 6 (PA6) composite samples. By implementing a mold temperature significantly higher than the typical molding practice, which rarely exceeds 100°C, we assess the effects of advanced mold temperature management. The research introduces a newly engineered mold structure specifically designed for localized mold heating, distinguishing it as the 'novel cavity.' This innovative design is compared against traditional molding methods to highlight the improvements in weld line strength at elevated mold temperatures. To optimize the molding parameters, we apply an Artificial Neural Network (ANN) in conjunction with a Genetic Algorithm (GA). Our findings reveal that the optimal ultimate tensile strength (UTS) and elongation values are achieved with a filling time of 3.4 seconds, packing time of 0.8 seconds, melt temperature of 246°C, and a novel high mold temperature of 173°C. A specific sample demonstrated the best molding parameters at a filling time of 3.4 seconds, packing time of 0.4 seconds, melt temperature of 244°C, and mold temperature of 173°C, resulting in an elongation value of 582.6% and a UTS of 62.3 MPa. The most influential factor on the PA6 sample's UTS and elongation at the weld line was found to be the melt temperature, while the filling time had the least impact. SEM analysis of the fracture surfaces revealed ductile fractures with rough surfaces and grooves, indicative of the weld line areas' bonding quality. These insights pave the way for significant improvements in injection molding conditions, potentially revolutionizing the manufacturing process by enhancing the structural integrity of the weld lines in molded PA6 samples.

BackgroundThis study aimed to measure the preferences for mental health support among health professionals, their willingness to support the mental health of colleagues and associated factors.MethodA descriptive cross-sectional study was performed from August to October 2022 within five hospitals located in Hanoi, Vietnam. A total of 244 health professionals participated in the study. Data on socio-economic status, health and COVID-19-related characteristics, Depression Anxiety Stress Scale (DASS-21); and preferences for mental health support services were collected by using a structured self-reported questionnaire. Multivariate logistic regression models were utilized to identify associated factors with the demand for mental support services.Results13.9%, 17.1% and 8.6% reported having at least mild depression, anxiety and stress, respectively. There 13.9% did not seek any mental health support during the COVID-19 pandemic. The most common support included talking with friends (52.9%), family (50.8%), colleagues (47.6%) and using social networks/Internet (43.5%). There 31.1% had been aware of mental health services, but only 18.0% used this service at least once. Regarding preferences, 47.3% had a demand for mental support services, and the most preferred service was providing coping skills (25.9%), followed by skills to support others against mental problems (22.2%). Major sources of support included psychiatrists (34.4%), colleagues (29.1%) and family (27.9%). The main preferred channels for support included telephone/mobile phone (35.7%) and Internet (20.9%). Only 12.3% were willing to provide mental support for colleagues during the pandemic. Age, education, perceived mental health status, ever seeking any mental service, and DASS-21 depression score were associated with demand for mental support services.ConclusionThis study found a lack of awareness of mental health services for health professionals, as well as moderate levels of demand for this service in this population. Raising awareness and developing tailored mental health support services are important to enhancing the mental well-being of health professionals in Vietnam to prepare for the next pandemic.

AbstractDrawing upon the resource-based view, this study examines the effects of two key elements of market orientation, i.e. customer orientation and competitor orientation, on the use of management accounting information to enhance firm performance. The partial least squares structural equation modeling (PLS-SEM) results from data collected from top- and mid-level managers working in 279 large companies in Vietnam indicate both customer and competitor orientation have positive effects on the use of management accounting systems (in terms of broad scope, timeliness, aggregation, and integration). This, in turn, enhances firm performance. The study’s results provide various theoretical and managerial implications for building and developing management accounting systems for Vietnamese companies.

Trang Thi Hanh Do,1 Quyen Thi Tu Bui,2 Bui Thi Thu Ha,3 Thi Minh Le,3 Vui Thi Le,3 Quynh-Chi Thai Nguyen,3 Kimberly Joyce Lakin,4 Tung Thanh Dang,5 Loi Van Bui,5 Thien Cong Le,5 An Thi Ha Tran,5 Hien Thi Thu Pham,5 Tuan Van Nguyen5 1Faculty of Environmental and Occupational Health, Hanoi University of Public Health, Hanoi, Vietnam; 2Faculty and Fundamental Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 3Faculty of Social and Behavioral Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 4Nossal Institute for Global Health, Melbourne School of Population and Global Health, Melbourne, Victoria, Australia; 5The National Institute of Mental Health, Bach Mai Hospital, Hanoi, VietnamCorrespondence: Quyen Thi Tu Bui, Department of Biostatistics, Faculty of Fundamental Sciences, Hanoi University of Public Health, 1A Duc Thang Street, Bac Tu Liem District, Hanoi, Vietnam, Tel +84 912 225 245, Fax +84 24 6266 2385, Email btq@huph.edu.vnPurpose: Detection of antenatal common mental disorders in low-resource settings like Vietnam is important and requires a reliable, valid and practical screening tool. Currently, there is no such tool validated for use among pregnant women in Vietnam. This study aims to assess the validity of the Vietnamese version of the 20-item Self Reporting Questionnaire (SRQ-20) by evaluating its reliability, factorial structure, and performance in detecting common mental disorder (CMD) symptoms, thereby identifying the optimum cut-off score for CMD screening among pregnant women in Vietnam.Participants and Methods: A total of 210 pregnant women from four rural communes participated in a face-to-face interview using the Vietnamese version of the SRQ-20, followed by a clinical diagnostic interview based on ICD-10 diagnostic criteria of CMDs. The reliability of the SRQ-20 was assessed by calculating the scale’s Cronbach’s alpha to measure internal consistency. Factor analyses were undertaken to examine the factor structure of the instrument. The Receiver Operating Characteristic (ROC) curve analysis was performed to assess the performance of the SRQ-20 against the clinical diagnosis and to identify the optimum cut-off score.Results: Internal consistency was good, with a Cronbach’s alpha of 0.87. Factor analyses resulted in a 4-factor solution. The area under the ROC curve (AUC) for detection of CMDs was 0.90. The optimum cut-off score of the SRQ-20 for detection of CMD symptoms among Vietnamese pregnant women was 5/6.Conclusion: The Vietnamese version of the SRQ-20 has the capacity to detect CMDs among pregnant women effectively and is recommended for use as a screening tool for CMDs in antenatal care settings in Vietnam.Keywords: SRQ-20, screening, common mental disorders, pregnant women, Vietnam

Landslides are a significant geological hazard that annually cause extensive damage and loss of life worldwide. Therefore, it is crucial to have reliable prediction models for landslide susceptibility in order to identify high-risk areas and implement proactive measures to prevent or mitigate their impacts. The purpose of this study is to propose and evaluate a novel approach called BCMO-DeepNeuralNets for spatial prediction of the areas most susceptible to landslides. The study focuses on a tropical cyclone area in three districts of central Vietnam, namely Nam Tra My, Bac Tra My, and Phuoc Son. Accordingly, ten input factors were considered: slope, aspect, elevation, relief amplitude, land use, soil type, road distance, geology, fault distance, and rainfall. The proposed BCMO-DeepNeuralNets method leverages the power of Deep Neural Networks (DNNs) to develop a deep-learning model capable of inferring landslide susceptibility indices. To optimize this model, the study employs the Balancing Composite Motions Optimization (BCMO) technique. Additionally, Logistic Regression (LR) and Support Vector Machines (SVM) are used as benchmark models to confirm the efficacy of the proposed approach. To evaluate the prediction performance of the models, popular metrics such as mean squared error (MSE), accuracy (Acc), and Area Under the ROC Curve (AUC) are employed. The results show that the BCMO-DeepNeuralNets model exhibits high prediction performance, with an MSE of 0.038, Acc of 93.4%, and AUC of 0.971. This model outperforms the benchmark models, LR and SVM. Consequently, BCMO-DeepNeuralNets emerges as a new tool with significant potential for landslide susceptibility mapping. The landslide susceptibility map generated by this research aids in the identification of landslide-prone areas, providing a more accurate understanding of associated risks. As a result, it can be instrumental in policy and decision-making processes, enabling the implementation of appropriate measures to mitigate the impact of landslides in the study area.

The Mekong Delta region of Vietnam has great potential for agricultural development thanks to natural incentives. Many livestock industries have developed for a long time and play an important role in the country with many agricultural export products. In the era of breakthrough technologies and advances in information technology, many techniques are used to support the development of smart agriculture. In particular, computer vision techniques are widely applied to help farmers save a lot of labour and cost. This study presents an approach for counting eels based on Mathematical Morphology Operations and Boundary Detection from images of breeding eels captured with the proposed photo box. The proposed method is evaluated using data collected directly from a breeding eel farm in Vietnam. The authors of the research evaluate and investigate the length distribution of eels to select the appropriate size for counting tasks. The experiments show positive results with an average Mean Absolute Error of 2.2 over a tray of more than 17 eels. The contribution of the research is to provide tools to support farmers in eel farms to save time and effort and improve efficiency.

Zinc–air batteries are promising candidates as stationary power sources because of their high specific energy density, high volumetric energy density, environmental friendliness, and low cost. The oxygen-related reactions at the air electrode are kinetically slow; thus, the air electrode integrated with an oxygen electrocatalyst is the most critical component, and inevitably determines the performance of a Zn–air battery. The aim of this paper was to document progress in researching bifunctional catalysts for Zn–air batteries. The catalysts are divided into several categories: noble metal, metal nanoparticle (single and bimetallic), multicomponent nanoparticle, metal chalcogenide, metal oxide, layered double hydroxide, and non-metal materials. Finally, the battery performance is compared and discussed.

Artificial intelligence (AI) technologies have seen strong development. Many applications now use AI to diagnose breast cancer. However, most new research has only been conducted in centralized learning (CL) environments, which entails the risk of privacy breaches. Moreover, the accurate identification and localization of lesions and tumor prediction using AI technologies is expected to increase patients’ likelihood of survival. To address these difficulties, we developed a federated learning (FL) facility that extracts features from participating environments rather than a CL facility. This study’s novel contributions include (i) the application of transfer learning to extract data features from the region of interest (ROI) in an image, which aims to enable careful pre-processing and data enhancement for data training purposes; (ii) the use of synthetic minority oversampling technique (SMOTE) to process data, which aims to more uniformly classify data and improve diagnostic prediction performance for diseases; (iii) the application of FeAvg-CNN + MobileNet in an FL framework to ensure customer privacy and personal security; and (iv) the presentation of experimental results from different deep learning, transfer learning and FL models with balanced and imbalanced mammography datasets, which demonstrate that our solution leads to much higher classification performance than other approaches and is viable for use in AI healthcare applications.

ABSTRACTWith the continuous development of a society in which construction works are ubiquitous, investors often accelerate the progress of construction projects and may neglect to follow the proposed construction processes. As a result, construction works decrease in durability and become more susceptible to cracking, breakages, or collapse, resulting in significant damage to people and property. Meanwhile, labour to monitor the progress of works is often time-consuming, effortful, prone to errors, and vulnerable to risk. This study introduces a system that uses a Wireless Sensor Network (WSN) consisting of a laser-ranging Time-of-Flight (ToF) sensor module for structural crack monitoring in architecture. The proposed system collects data on the variation in amplitude of an existing crack as well as environmental factors that can adversely affect cracks such as temperature, humidity, and vibration. This aids the assessment of safety levels by providing warnings and remedies as quickly as possible, contributing to raising the safety level for workers and people living in the area, and minimizing potential damage. Test results demonstrate the following advantages: the device operates stably in extreme weather conditions; has an expected lifetime of more than ten years; and features high accuracy, long data transmission, and low-cost, low-power operation.

In this study, we assessed a highly structured, yearlong, case-based course designed for undergraduate pre-health students. We incorporated both content learning assessments and developed a novel method called Multiple Mini Exams for assessing course impact on the development of skills that professional schools often seek in pre-health students, focusing on students' abilities to collaborate with others, display bedside manners, synthesize patient case details, appropriately use scientific and medical language, and effectively attain patients' medical histories. This novel method utilized a rubric based on desired medical student skills to score videotaped behaviors and interactions of students role playing as doctors in a hypothetical patient case study scenario. Overall, our findings demonstrate that a highly structured course, incorporating weekly student performance and presentation of patient cases encompassing history taking, diagnosis, and treatment, can result in content learning, as well as improve desired skills specific for success in medical fields.

In this study, the locomotive and the turnout were simulated with three-dimensional models. The purpose of this study is prediction the derailment phenomenon of the locomotive running on the turnout in Vietnam. Multi-body simulations were implemented by SIMPACK software to determine the derailment coefficient, the wheel unloading factor, and the lateral force. The interaction between the locomotive and the track structure at turnout was considered unified. The derailment coefficient, the wheel unloading factor, and the lateral force were calculated for locomotive-D19E at turnout (tg0.15) for 1000 mm gauge according to QCVN 18:2011/BGTVT, EN 14363:2016, and TCVN 8784:2011. The derailment coefficient, the wheel unloading factor, and the lateral force for the locomotive at max speed V=27.8 km/h are 0.94, 0.61, and 67.46 kN, respectively. These results show that the locomotive will not derail when it passes the turnout at a speed V 27.8 km/h.

In the process of analyzing prestressed sleepers for turnout on elastic foundations with large lengths, authors around the world often use the one-stiffness model of Winkler or the two-stiffness model of the Filonenko-Borodich and Pasternak. Railway bed stiffness is assumed to be a constant value from the design to the end of the operating process. In this study, the authors use a one-stiffness model to simulate the interaction between the prestressed sleepers for turnout and the railway bed, which helps to describe the contact and non-contact between the prestressed sleepers of turnout and the railway bed, the phenomenon that the many traditional models do not implement yet. In addition, the railway bed stiffness included in the analysis is obtained by testing at the field of a railway section in operation in Vietnam. The authors use the finite element method and combine it with the Newton iteration method. Accordingly, the analysis results of prestressed sleepers for turnout will be more consistent with actual behavior. We need to check the actual stiffness of the foundation before it is included in the analysis because the actual stiffness of the foundation gives more accurate results during analysis.

Currently, the prestressed concrete track slabs are testing installed at the field to replace the reinforced concrete track slabs at railroad crossings in Vietnam. Prestressed concrete track slabs used for 1000 mm gauge. The dimensions of each slab were designed with a length, width, and height of 2.5 m, 1.0 m, and 0.33m, respectively. This paper presents experimental measurements to analyze the behavior of the prestressed concrete track slab at railroad crossings under the action of train and truck dynamic load. Experiment measurements were implemented at the site. Eight strain gauges were mounted at three sections of the track slab to measure the deformation. HL-93 truck load and TY-7E train load were used in the test. The results show the eccentricity of the gravity center point of the prestressed wires to the centroidal axis of the transformed area. The deviation ratio between experimental and theoretical results is from 0.88% ÷ 1.25%. These results can be used to optimize the design process and limit cracks. In addition, tensile and compressive stresses in slabs are smaller than the allowable values. The results show that this track slab meets the requirements of durability and strength under the action of train and truck dynamic load.

Ultraviolet (UV)-based devices have shown their effectiveness on various germicidal purposes. To serve their design optimisation, the disinfection effectiveness of a vertically cylindrical UV lamp, whose wattage ranges from P = 30 − 100 W, is numerically investigated in this work. The UV radiation is solved by the Finite Volume Method together with the Discrete Ordinates model. Various results for the UV intensity and its bactericidal effects against several popular virus types, i.e., Corona-SARS, Herpes (type 2), and HIV, are reported and analysed in detail. Results show that the UV irradiance is greatly dependent on the lamp power. Additionally, it is indicated that the higher the lamp wattage employed, the larger the bactericidal rate is observed, resulting in the greater effectiveness of the UV disinfection process. Nevertheless, the wattage of P ≤ 100W is determined to be insufficient for an effective disinfection performance in a whole room; higher values of power must hence be considered in case intensive sterilization is required. Furthermore, the germicidal effect gets reduced with the viruses less sensitive to UV rays, e.g, the bactericidal rate against the HIV virus is only ∼8.98% at the surrounding walls.

The accounting literature is silent about how digital transformation can be transformed into outstanding business performance through cloud-based accounting effectiveness (CBAE) under the supervision of digital leaders. In the digital age, this mechanism is practically significant for emerging market firms to promote accounting practices and decision-making effectiveness. Thus, this study investigates how CBAE and decision-making quality (DMQ) mediate the impact of digital transformation on firm performance. In addition, the moderating effects of digital leadership on the relationships between digital transformation and CBAE and those between CBAE and DMQ are investigated. The proposed model and its hypotheses are evaluated using partial least squares structural equation modeling (PLS-SEM) on survey data from 252 large-sized Vietnamese firms. The study's findings are as follows: (1) digital transformation positively affects CBAE, which affects DMQ and firm performance; (2) when digital leadership is strong, the effects of digital transformation on CBAE and CBAE on DMQ are amplified. These findings demonstrate how the interaction between digital transformation and digital leadership can contribute to the success of firms in emerging markets that use cloud accounting. In addition, the current study elucidates the mechanism by which digital transformation influences the digitalization of accounting practices and contributes to our understanding of digital transformation research in accounting by introducing digital leadership as a boundary condition.

Export market orientation has always been a central issue in the study of international business and marketing. Therefore, it is essential to put effort into understanding the concept and its application in real-life business. Following in-depth studies and critical review papers in previous years, this research paper focuses on synthesizing and analyzing empirical studies in the period from 2015 to 2021, revolving around theoretical issues, context, the main features of the object, and the methodology. Within a predetermined time frame, twenty critical papers on export market orientation were selected and analyzed. The result reveals in detail that there are sixteen antecedents and five outcomes of export market orientation, as well as a total of eight moderators and seven mediators influencing their relationships. Findings have indicated that in recent years, models and theories of earlier eras have been developed and many of them have reached maturity.

The in-traffic microenvironment can enhance personal exposure to fine particulate matter (PM). With this study, we aimed to calibrate a DustTrak instrument (DustTrak 8533 DRX Aerosol Monitor, TSI Incorporated, Shoreview, MN, USA) and low-cost sensors (AS-LUNG-P sensors) and then assess inhalation exposure to PM2.5 and PM1 for different commuters in central areas of Ho Chi Minh City (HCM). The DustTrak instrument and low-cost sensors were calibrated using a gravimetric method under side-by-side conditions. Relationships between the DustTrak signals and PM concentrations measured by the gravimetric method were identified using simple linear regression models for PM2.5 (R2 = 0.998, p-value < 0.05) and PM1 (R2 = 0.989, p-value < 0.05). Meanwhile, PM concentrations determined by the AS-LUNG-P sensors and the gravimetric method were correlated using two-segmented linear regressions. To obtain the corresponding two-segment regression equations, the response of the AS-LUNG-P sensors was compared with the corrected DustTrak data. The coefficient of variation (CV) evaluated for all sensors was smaller than 10%, indicating that the data were applicable for particle assessment. For inhalation exposure assessment, the results showed that commuters using open transport modes, such as bikes, motorbikes, and walking, were exposed to more PM than those using closed transport modes (e.g., cars). Specifically, the bicyclists had the highest inhaled doses of PM among the open transport groups. PM exposure levels in the morning were higher than in the afternoon. Additionally, exposure levels to PM concentrations rapidly increased when passing through intersections of major roads and moderately decreased when using surgical facemasks.

Fluency development is one of the prominent components of a well-balanced language course. In particular, speaking fluency interests second language (L2) researchers who have continuously tried to look for the finest approach for helping L2 learners attain a certain level of fluency to achieve effective communication. To this end, the primary purpose of this research is to explore what factors affect English-speaking fluency and what strategies can effectively boost its development among a cohort of Vietnamese university students. The participants were 142 English majors who filled out a questionnaire measuring their perceived self-efficacy concerning English-speaking fluency. Four teachers and six students joined the semi-structured interviews. Quantitative and qualitative data revealed that the most influential factors were linguistic elements, followed by performance, and affective factors. The most effective strategy for enhancing fluency was task repetition. Notably, the findings revealed a mismatch between teachers' and students' understandings of speaking fluency, which may negatively impact the achievement of fluent speech. Based on these results, pedagogical implications are discussed for English teachers and students regarding fluency development.

Students' scientific literacy may be improved by the integration of social issues into biology courses, enabling them to make informed decisions on social issues in the context of their scientific knowledge. Additionally, this may allow students to recognize the connection between science and society. Although there are a number of benefits with having students learn about social issues in biology courses, most undergraduate courses may follow a traditional curriculum, which emphasizes the scientific content without framing it in a social context. Here, we investigated whether undergraduate students have been exposed to social issues in previous biology courses and examined how their perceptions changed before and after taking a biology course that incorporated social issues. In surveys, most students reported having no exposure to social issues in biology courses. Most students, especially females and persons excluded because of their ethnicity or race (PEERs), agreed with the integration of social issues in biology courses before taking the course. Students found reflection essays to be a useful tool in allowing them to think and share their thoughts on social issues as well as relate the course content to their personal lives. These results highlight students' interest in learning about social issues from a scientific perspective and how reflection essays may be used to practice applying their knowledge to real-world issues.

Influenza burden estimates are essential to informing prevention and control policies. To complement recent influenza vaccine production capacity in Vietnam, we used acute respiratory infection (ARI) hospitalization data, severe acute respiratory infection (SARI) surveillance data, and provincial population data from 4 provinces representing Vietnam’s major regions during 2014–2016 to calculate provincial and national influenza-associated ARI and SARI hospitalization rates. We determined the proportion of ARI admissions meeting the World Health Organization SARI case definition through medical record review. The mean influenza-associated hospitalization rates per 100,000 population were 218 (95% uncertainty interval [UI] 197–238) for ARI and 134 (95% UI 119–149) for SARI. Influenza-associated SARI hospitalization rates per 100,000 population were highest among children 65 years of age (207; 95% UI 186–227), underscoring the need for prevention and control measures, such as vaccination, in these at-risk populations.

Purpose – Groundwater plays a critical part in both natural and human existence. When surface water is scarce in arid climates, groundwater becomes an immensely valuable resource. Dak Lak is an area that frequently lacks water resources for everyday living and production, and the scarcity of water resources is exacerbated during the dry season. As a result, it is critical to do study and understand about groundwater to meet the region's water demand. This study aims to extend the use of the MODFLOW model for groundwater simulation and assess the overall groundwater reserves and water demand in the highland province Dak Lak. Design/methodology/approach – The MODFLOW model is used in this work to compute and analyze the flow, prospective reserves of groundwater from which to plan extraction and estimate groundwater variation in the future. Findings – The application of the MODFLOW model to Dak Lak province demonstrates that, despite limited data, particularly drilling hole data for subterranean water research, the model's calculation results have demonstrated its reliability and great potential for use in other similar places. The use of the model in conjunction with other data extraction modules is a useful input for creating underground flow module maps for various time periods. The large impact of recharge and evaporation on groundwater supplies and water balance in the research area is demonstrated by simulations of climate change scenarios RCP4.5 and RCP8.5. Originality/value – None of the studies has been done previously to analyze water resources of Dak Lak and the scarcity of water resources is exacerbated during the dry season. Therefore, this study will provide useful insights in the water resource management and the conservation of Dak Lak. The groundwater in Dak Lak can meet the area's water demand, according to the results obtained and water balance in the study area. However, the management of water resources and rigorous monitoring of groundwater extraction activities in the area should receive more attention.

ImidazoleTriflate (ITF)—SiO2 composites xITF-(100-x)SiO2 were prepared by planetary ball-milling method. The structure of the composites was studied by TG-DTA, XRD and FT-IR methods. The ionic conductivity was assessed by alternative current (AC) electrochemical impedance measurement. From XRD results, the crystal size of imidazole triflate became smaller as its loading in xITF-(100-x)SiO2 decreased. With x = 40 weight percent, the calculated crystal size was about 7.3 nm. FT-IR spectra showed the existence of CF3SO3-···H+ ion aggregates and imidazole—proton bonds. The electrical conductivity of sample x = 50 was about 6.3 × 10−3 Scm−1 at 130 °C under anhydrous condition and about 30 times higher than that of sample x = 100.

In the robotic applications, wheel-legged robot provides many advantages such as lightweight, superior performance, low energy consumption and wide development prospects. Especially, in some emergent cases, it has the ability to move flexibly, pass obstacles and work in both outdoor and indoor environment. In this paper, a novel design of wheel-legged platform for robotic investigation is presented. Firstly, the principle diagram of underactuated robot (UR) is outlined owing to the model of inverted pendulum. Then, several theoretical computations are suggested to ensure the ability to work as its desired control. Later, an overview of mechanical design in 3D is described as well as the design of electrical circuit is demonstrated based on these requirements. To validate the feasibility and effectiveness of our approach, several numerical simulations are carried out.

Freshwater shrimp of the family Atyidae De Haan, 1849 have been studied in Vietnam for more than a century. A total of 24 species of atyid shrimps from the genera Caridina H. Milne Edwards, 1837, Neocaridina Kubo, 1938, Atyopsis Chace, 1983 have been recorded from Vietnam. With 22 species, the majority are from the genus Caridina. In 2013, Karge and Klotz mentioned the occurrence of four yet undescribed species belonging to Paracaridina Liang, Guo & Tang, 1999 in Vietnam without taxonomic details.In general, studies of freshwater atyids in Vietnam are limited and most Vietnamese taxa await a taxonomic revision. The available data do not fully reflect their estimated species diversity in the country and distribution data are deficient. Here, we focus on four species of atyid shrimps from two genera, viz. Caridina cantonensis Yu, 1938, C. lanceifrons Yu, 1936, C. serrata Stimpson, 1860 and Neocaridina palmata (Shen, 1948), all described from China and have been reported to occur in Vietnam. The previous reports on the occurrence of these species in Vietnam are largely unreliable due to taxonomic confusion. To contribute to the knowledge of these taxa, we provide the first verified distribution records of the four species in the country with some taxonomic remarks.This study shows the first taxonomically-verified distribution data of four atyid shrimp species originally described from China, but also reported from Vietnam, albeit under various species names and, in some cases, erroneously. These data allow the first meaningful discussion of the distribution in light of the reproductive strategy of these four species and, in conjunction with the taxonomic remarks, will contribute to the knowledge of these taxa. As a result of this research and data from previous studies, we now consider all four species as widespread and non-endemic, but land-locked (with a complete freshwater life cycle). In Vietnam, all four species are confined to the northern half of the country. Beyond Vietnam, we provide the first records for Caridina lanceifrons from southern Thailand, which suggests a major sampling gap in Indochina.